一句话：首次从风险视角刻画 AI 模型供应链，关注基座模型经微调、量化、合并形成衍生模型的现象。
论文关注 Hugging Face 等平台上公开 AI 模型构成的复杂供应链：基座模型不断通过微调、量化、合并等操作转化为衍生模型。该供应链对模型复用、来源追溯和风险管理至关重要，但目前仍缺乏大规模刻画。作者从风险视角开展初步研究，试图揭示模型供应链的形态与潜在风险。摘要仅给出问题动机与研究对象，未说明具体数据集、分析方法或实证结果，因此其核心贡献在于提出并界定模型供应链的风险研究问题。
The rapid proliferation of publicly available artificial intelligence models on platforms such Hugging Face has formed a complex model supply chain, where base models are continually transformed, e.g., by fine-tuning, quantization, and merging, into new derived models. Despite its critical importance for reuse, provenance, and risk management, this supply chain remains poorly characterized at scale.
@inproceedings{paperbot11700,
title = {A First Look at Model Supply Chain: From the Risk Perspective},
author = {Ziqian Chen and Zekai Chen and Susheng Wu and Bihuan Chen and Wenyan Song and Yiheng Huang and Zhuotong Zhou and Yiheng Cao and Xin Peng},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3773171}
}